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Verify

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codeaholicguy
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AI DevKit · Enforce evidence-based completion claims — require fresh command output before reporting success. Use when completing any task, fixing a bug, finishing a phase, running tests, building, deploying, or making any "it works" claim.

Overview

Publishercodeaholicguy
Repositoryai-devkit
Skill nameverify
Stars
1.6K
Forks
252
Bundled files
1
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by codeaholicguy on GitHub. Read the source before you install it.

Installation

Install the Verify AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/codeaholicguy/ai-devkit.git /tmp/ai-devkit
mkdir -p .claude/skills
cp -r /tmp/ai-devkit/skills/verify .claude/skills/verify
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Verify in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Verify on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Verify is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Verify

Prove it works before saying it works.

Hard Rules

  • Do not claim completion without fresh terminal evidence from this session.
  • Forbidden words in completion claims: "should", "probably", "seems to", "likely", "I believe", "I think it works". These signal unverified assertions.
  • Cached, remembered, or previous-session output is not evidence. Run it again.

Gate Function

Every completion claim must pass all 5 steps in order:

  1. Identify — What command proves this claim? If multiple commands are needed, run the gate once per command.
  2. Run — Execute the full command now. No partial runs, no skipping.
  3. Read — Read complete output. Check exit code. Count pass/fail.
  4. Confirm — Does the output prove the exact claim?
  5. Report — State the result, cite command, exit code, and key output.

If any step fails, stop. Fix the issue and restart from step 1.

If no verification command exists (e.g., no test suite), tell the user and ask them how to verify before claiming done.

Verification Patterns

ClaimRequired EvidenceNot Sufficient
Tests passTest output: 0 failures, exit 0Previous run, "should pass now"
Build succeedsBuild output: exit 0Linter passing, partial build
Bug is fixedReproduce symptom → now passes"Changed code, should be fixed"
Linter cleanLinter output: 0 errorsSingle file check
Phase completeEach criterion verified individually"Tests pass, so done"
Feature worksE2E test or manual walkthroughUnit tests alone

Regression Verification

For bug fixes, a single pass is not enough:

  1. Write a test covering the bug.
  2. Run → must pass (fix in place).
  3. Revert the fix.
  4. Run → must fail (proves test catches the bug).
  5. Restore the fix.
  6. Run → must pass.

If step 4 passes, the test is wrong. Rewrite it.

Red Flags and Rationalizations

RationalizationWhy It's WrongDo Instead
"This change is trivial"Trivial changes break things constantlyRun the check
"I ran it earlier"Code changed since thenRun it again now
"The test is flaky"Flaky ≠ ignorableFix the flake first
"It compiles, so it works"Compilation ≠ correctnessRun the tests
"The CI will catch it"CI is a safety net, not a substituteVerify locally first
"The agent said it's done"Agent claims need verification tooCheck diff and run tests

Memory Integration

After a failed verification, store the failure pattern: npx ai-devkit@latest memory store --title "<failure pattern>" --content "<what failed and how to avoid>" --tags "verify,failure-pattern"

Task Tracing

If a task name is known and tracing is usable, record task evidence after the verification report per task. If tracing was not probed, run the real read probe first. If probe or evidence recording fails, report the failed task command and continue verification; never block verification on optional task logging.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Verify AI skill do?

AI DevKit · Enforce evidence-based completion claims — require fresh command output before reporting success. Use when completing any task, fixing a bug, finishing a phase, running tests, building, deploying, or making any "it works" claim.

Why use Verify on TypingMind?

Because you install it once and use it with any model. Verify is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Verify in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/codeaholicguy/ai-devkit/tree/main/skills/verify. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Verify?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Verify?

As many as you like. As long as a model supports skills, you can use Verify with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Verify AI skill free?

Yes. It is published on GitHub by codeaholicguy under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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